Redwine quality Analysis by Gangadhara Naga Sai

## [1] "Names of variables "
##  [1] "fixed.acidity"        "volatile.acidity"     "citric.acid"         
##  [4] "residual.sugar"       "chlorides"            "free.sulfur.dioxide" 
##  [7] "total.sulfur.dioxide" "density"              "pH"                  
## [10] "sulphates"            "alcohol"              "quality"
## [1] "Dimensions of wine data"
## [1] 1599   12
## [1] "Structure of wine data"
## 'data.frame':    1599 obs. of  12 variables:
##  $ fixed.acidity       : num  7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
##  $ volatile.acidity    : num  0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
##  $ citric.acid         : num  0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
##  $ residual.sugar      : num  1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
##  $ chlorides           : num  0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
##  $ free.sulfur.dioxide : num  11 25 15 17 11 13 15 15 9 17 ...
##  $ total.sulfur.dioxide: num  34 67 54 60 34 40 59 21 18 102 ...
##  $ density             : num  0.998 0.997 0.997 0.998 0.998 ...
##  $ pH                  : num  3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
##  $ sulphates           : num  0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
##  $ alcohol             : num  9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
##  $ quality             : int  5 5 5 6 5 5 5 7 7 5 ...
## [1] "Summary of Redwine data"
##  fixed.acidity   volatile.acidity  citric.acid    residual.sugar  
##  Min.   : 4.60   Min.   :0.1200   Min.   :0.000   Min.   : 0.900  
##  1st Qu.: 7.10   1st Qu.:0.3900   1st Qu.:0.090   1st Qu.: 1.900  
##  Median : 7.90   Median :0.5200   Median :0.260   Median : 2.200  
##  Mean   : 8.32   Mean   :0.5278   Mean   :0.271   Mean   : 2.539  
##  3rd Qu.: 9.20   3rd Qu.:0.6400   3rd Qu.:0.420   3rd Qu.: 2.600  
##  Max.   :15.90   Max.   :1.5800   Max.   :1.000   Max.   :15.500  
##    chlorides       free.sulfur.dioxide total.sulfur.dioxide
##  Min.   :0.01200   Min.   : 1.00       Min.   :  6.00      
##  1st Qu.:0.07000   1st Qu.: 7.00       1st Qu.: 22.00      
##  Median :0.07900   Median :14.00       Median : 38.00      
##  Mean   :0.08747   Mean   :15.87       Mean   : 46.47      
##  3rd Qu.:0.09000   3rd Qu.:21.00       3rd Qu.: 62.00      
##  Max.   :0.61100   Max.   :72.00       Max.   :289.00      
##     density             pH          sulphates         alcohol     
##  Min.   :0.9901   Min.   :2.740   Min.   :0.3300   Min.   : 8.40  
##  1st Qu.:0.9956   1st Qu.:3.210   1st Qu.:0.5500   1st Qu.: 9.50  
##  Median :0.9968   Median :3.310   Median :0.6200   Median :10.20  
##  Mean   :0.9967   Mean   :3.311   Mean   :0.6581   Mean   :10.42  
##  3rd Qu.:0.9978   3rd Qu.:3.400   3rd Qu.:0.7300   3rd Qu.:11.10  
##  Max.   :1.0037   Max.   :4.010   Max.   :2.0000   Max.   :14.90  
##     quality     
##  Min.   :3.000  
##  1st Qu.:5.000  
##  Median :6.000  
##  Mean   :5.636  
##  3rd Qu.:6.000  
##  Max.   :8.000

Univariate Plots Section

Quality ranges from 0 to 10,but in data minimum is 3 and maximum is 8, which means that most of the wines we will look at in the analysis are average wines.

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.000   5.000   6.000   5.636   6.000   8.000

Univariate Analysis

What is the structure of your dataset?

Data set is regarding the wine quality and several chemical componets that it contains.there ae 1599 samples of wine with 10 variables(fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfer dioxide, density, pH, sulphates, alcohol, quality) of type numeric and 1 rating factor quality of type int.

What is/are the main feature(s) of interest in your dataset?

Quality is the main feature of insterest ,given by 3 wine experts according to their knowledge and experience.Quality ranges from 0 to 10 but our data has least quality of 3 and highest quality of 8. Lets find out what are the main deciding factors for high quality wine.

What other features in the dataset do you think will help support your investigation into your feature(s) of interest?

There can lot more features since in real world so many factors affect the quality of Red wine. >Type of grapes used >flavor (like combination of different ingredients) >Color >taste(sweet,sour,bitter,etc) >total cost from the ingredients to final production of wine(since cost matters since high quality wine with less cost really matters)

Did you create any new variables from existing variables in the dataset?

Yes , i made total.acidity and combined.sulphur.dioxide, which may show some unseen trends.

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

Volatile acidity is having a bimodal distribution and Citric acid has quite a long-tail distribution.But it is not a Normal Distribution. the data was already tidy so there was no requirement for any adjustment.

Bivariate Plots Section

By using correlation we can find out some important insights among variables

## [1] "Correlation among the variables"
##     volatile.acidity total.sulfur.dioxide              density 
##          -0.39055778          -0.18510029          -0.17491923 
##            chlorides                   pH  free.sulfur.dioxide 
##          -0.12890656          -0.05773139          -0.05065606 
##       residual.sugar        fixed.acidity          citric.acid 
##           0.01373164           0.12405165           0.22637251 
##            sulphates              alcohol              quality 
##           0.25139708           0.47616632           1.00000000

Observing the correlation, alcohol and volatile acidity, have a higher correlation with the quality of wine.Suphates and citric acid are also correlated with the quality of wine. Residual sugar has almost no correlation with quality.

Bivariate Analysis

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

Quality being the feature of interest,the correlation between quality and each different variable in the dataset are examined.Quality of wine is directly proportional to the alcohol content and volatile acidity and inversely proportional to density,total sulfur dioxide and chlorides.

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

pH and volatile acidity are positively correleated, Higher the pH value means less acidity, but from plots a higher volatile acidity means more acidity. Density of wine has high negative correlation with the amount of alcohol in wine. I was expecting a close relation between sulphur and sulphur dioxide,there seems no relation with correlation coefficient of 0.04.

What was the strongest relationship you found?

correlation of quality with other variables volatile.acidity total.sulfur.dioxide density chlorides -0.39055778 -0.18510029 -0.17491923 -0.12890656 pH free.sulfur.dioxide residual.sugar fixed.acidity -0.05773139 -0.05065606 0.01373164 0.12405165 citric.acid sulphates alcohol quality 0.22637251 0.25139708 0.47616632 1.00000000 From the correlations we can clearly see alcohol positiely and volatile.acidity negitively are having a strong relation with quality. And density and fixed acidity have a strong correlation.

Multivariate Plots Section

Multivariate Analysis

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?

Were there any interesting or surprising interactions between features?

OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.


Final Plots and Summary

Plot One

Description One

Plot Two

Description Two

Plot Three

Description Three


Reflection